Random Forest Estimation of the Ordered Choice Model

Michael Lechner and Gabriel Okasa

Papers from arXiv.org

Abstract: In this paper we develop a new machine learning estimator for ordered choice models based on the random forest. The proposed Ordered Forest flexibly estimates the conditional choice probabilities while taking the ordering information explicitly into account. In addition to common machine learning estimators, it enables the estimation of marginal effects as well as conducting inference and thus provides the same output as classical econometric estimators. An extensive simulation study reveals a good predictive performance, particularly in settings with non-linearities and near-multicollinearity. An empirical application contrasts the estimation of marginal effects and their standard errors with an ordered logit model. A software implementation of the Ordered Forest is provided both in R and Python in the package orf available on CRAN and PyPI, respectively.

Date: 2019-07, Revised 2022-09
New Economics Papers: this item is included in nep-big, nep-cmp, nep-dcm and nep-pay
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Citations: View citations in EconPapers (1)

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http://arxiv.org/pdf/1907.02436 Latest version (application/pdf)

Related works:
Journal Article: Random Forest estimation of the ordered choice model (2025)
Working Paper: Random Forest Estimation of the Ordered Choice Model (2019)
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